1. Field
This disclosure relates to compression of video sequences, more particularly compressing the images using predictive coding techniques with motion estimation.
2. Background
Transmission of video content in digital form opens up many avenues of broadcast that were not previously available. Information associated with the video content can now be transported along with the content, allowing programming guides, program summaries and other information to be made available to users. Video data can be packaged into the various data ‘slices’ such as Internet Protocol packets, Frame Relay frames, etc., and routed across data networks.
One drawback of digital video data is that it consumes rather large amounts of bandwidth unless compressed. Compression and decompression of the data allows the images to be reproduced without using as much bandwidth. Most compression techniques strive to provide the largest amount of compression with the lowest amount of error. The tradeoff between reconstructed image quality and the amount of compression obtainable is the focus of many different types of compression techniques.
One such technique is that which will be referred to here as predictive coding. An example of this technique is that which is used in the Moving Pictures Experts Group (MPEG) standards. In these techniques, the frames of the video sequences are determined to be either I, P, or B, pictures. I pictures are intracoded pictures, coded without reference to other pictures. Moderate compression is achieved by reducing spatial redundancy, but not temporal redundancy. They can be used periodically to provide access points in the bit stream where decoding can begin. P frames or pictures are predictive pictures and can use the previous I- or P-picture for motion compensation and may be used as a reference for further prediction. P-pictures offer increased compression compared to I-pictures. B pictures are bidirectionally-predictive pictures and can use the previous and next I- or P-pictures for motion-compensation, and offer the highest degree of compression.
Motion estimation allows the reconstruction of the pictures to reduce temporal redundancy between successive frames. In this manner, information that is repeated between frames is not unnecessarily repeated. Many different types of motion compression methods exist. However, they typically are all very complex and require significant computation power and resources to accurately portray motion between the frames. Another tradeoff exists here, between accuracy and complexity. More accurate motion compensation provides more accurate reconstruction with better compression. However, more accurate motion compensation requires more complex motion estimation techniques. This in turn results in either more complex hardware or more powerful software processing methods.
It would be useful to have accurate motion estimation techniques that do not require the increased complexity of current techniques.